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narendasan opened this issue Mar 13, 2020 · 0 comments · Fixed by #48
Closed

Grab dependencies automatically #14

narendasan opened this issue Mar 13, 2020 · 0 comments · Fixed by #48
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feature request New feature or request

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@narendasan
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We should either look at containerizing the build, or find someway to pull TensorRT, CUDA etc. with bazel. We already do this with libtorch. This should make it less likely builds will fail because of peoples environment

@narendasan narendasan added the feature request New feature or request label Mar 13, 2020
narendasan added a commit that referenced this issue Apr 29, 2020
7.0.0)

- Closes #42
- Issue #1 is back, unknown root cause, will follow up with the PyTorch
Team
- Closes #14: The default build now requires users to grab the tarballs
from the NVIDIA website to support hermetic builds, may look at some
methods to smooth this out later. The old method is still available
- New operators need to be implemented to support MobileNet in 1.5.0
(blocks merge into master)

Signed-off-by: Naren Dasan <[email protected]>
Signed-off-by: Naren Dasan <[email protected]>
frank-wei pushed a commit that referenced this issue Jun 4, 2022
Summary:
Pull Request resolved: https://github.com/pytorch/fx2trt/pull/14

Add support for torch.baddbmm. Add unit test to cover bmm and addmm, both are mapping to other converter insteand of having new acc_op converter.

Reviewed By: yinghai

Differential Revision: D34743279

fbshipit-source-id: efd417b2b494635c63f2ec58daa3fc568f72111a
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